计算机应用

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集成学习在粒子群优化算法改进中的应用研究

施彦   

  1. 北京工商大学信息工程学院
  • 收稿日期:2008-09-16 修回日期:1900-01-01 发布日期:2009-03-01 出版日期:2009-03-01
  • 通讯作者: 施彦

Ensemble learning application in improvement of particle swarm optimization algorithm

Shi Yan 施彦   

  • Received:2008-09-16 Revised:1900-01-01 Online:2009-03-01 Published:2009-03-01
  • Contact: Shi Yan 施彦

摘要: 集成学习通过结合多个学习者可以获得更好的结果。从三个方面,即从粒子级和维度级上进行集成,全过程或阶段性使用集成以及在单种群或多子种群中应用集成,研究了集成学习用于改进粒子群优化(PSO)算法的方式。通过三个典型函数优化问题的实验结果表明,与标准PSO和一些改进方法相比,集成学习可以改进PSO算法性能。

关键词: 粒子群优化算法, 集成学习, 智能计算

Abstract: Ensemble learning is a methodology that combines multiple individual learners, and can get better results. In this paper, ensemble learning was applied to improve the performance of Particle Swarm Optimization (PSO) algorithm in three aspects: on particle level or dimension level; in the whole iteration process or periodical iteration process; on single population or multiple subpopulations. The experimental results of three typical function optimization problems show that PSO algorithm based on ensemble learning can improve performance compared with the standard PSO and some improved PSO algorithms.

Key words: Particle Swarm Optimization algorithm (PSO), ensemble learning, intelligence computation